Analysis of contrast-enhanced MR images to assess renal function |
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Authors: | N. Michoux J-P. Vallée A. Pechère-Bertschi X. Montet L. Buehler B. E. Van Beers |
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Affiliation: | (1) Diagnostic Radiology Unit, Université Catholique de Louvain, St-Luc University Hospital, Avenue Hippocrate 10, 1200 Brussels, Belgium;(2) Digital Imaging Unit, Department of Radiology, University Hospital of Geneva, Geneva, Switzerland;(3) Department of Endocrinology, University Hospital of Geneva, Geneva, Switzerland;(4) Department of Nephrology, University Hospital of Geneva, Geneva, Switzerland |
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Abstract: | The image analysis and kinetic modeling methods used in dynamic contrast-enhanced magnetic resonance imaging of the kidney are reviewed. Image analysis includes various techniques of coregistration and segmentation. Few methods have been completely implemented. Nevertheless, the use of coregistration may become a standard to decrease the effect of motion on abdominal images and improve the quality of the renal signals. Kinetic models are classified into three categories: enhancement-based, external and internal representations. Enhancement-based representations are limited to a basic analysis of the tracer concentration curves in the kidneys. Their relationship to the underlying physiology is complex and undefined. However, they can be used to evaluate the split renal function. External representations assess the kidney input and output. An external representation based on the up-slope of the renal enhancement to calculate the renal perfusion is commonly used because of its simplicity. In contrast, external representation based on deconvolution or identification methods remain underexploited. For glomerular filtration, an internal representation based on a two-compartmental model is mostly used. Internal representations based on multi-compartmental models describe the renal function in a more realistic way. Because of their numerical complexity, these models remain rarely used. |
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Keywords: | Kidney Contrast-enhanced MRI Renal perfusion Glomerular filtration rate Image processing Theoretical models |
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